Comparison of murine and human estrogen sulfotransferase inhibition in vitro and in silico—Implications for differences in activity, subunit dimerization and substrate inhibition

Stjernschantz, Eva; Reinen, Jelle; Meinl, Walter; George, Beena J.; Glatt, Hansruedi; Vermeulen, Nico; Oostenbrink, Chris

doi:10.1016/j.mce.2009.12.001

Cited by 17 publications

(13 citation statements)

References 61 publications

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“…Previously, it has been demonstrated that PAPS and PAP have equivalent stabilizing effects on these SULT isoforms [17,43]. Thus, we used PAP (substituting PAPS) in order to generate protein conformations capable to accommodate substrates and inhibitors.…”

Section: Resultsmentioning

confidence: 99%

“…We achieved very good predictive results using docking-scoring into the best MD conformations for SULT1A1 and SULT1E1. Recently, Stjernschantz and co-workers [43] screened experimentally 34 potential endocrine-disrupting compounds on the murine and human SULT1E1 to identify selective inhibitors of the human enzyme. They then docked the identified active compounds using the software GOLD [53] and performed subsequent MD simulations of the docked complexes.…”

Section: Discussionmentioning

confidence: 99%

“…We used the all atoms PARAM27 force field [66,67]. All simulations were performed using monomer structures because previous MD studies for SULT1A1 and SULT1E1 suggested identical behavior for monomers and dimers [43,44]. For each SULT1 structure we kept the cofactor PAP.…”

Section: Methodsmentioning

confidence: 99%

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In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

et al. 2013

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Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

et al. 2013

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“…Comparison of murine and human oestrogen sulphotransferase inhibition in vitro and in silico, it suggested that catalytically inactive binding modes, other than the one observed in the crystal structures, be possible in SULT1E1 (Stjernschantz et al, 2010). 7008.…”

Section: (I) Individual Significance Analysis Resultsmentioning

confidence: 93%

A meta-analysis study of gene expression datasets in mouse liver under PPARα knockout

He¹,

Wang²,

Wang³

et al. 2013

Genet. Res.

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Gene expression profiling of peroxisome-proliferator-activated receptor α (PPARα) has been used in several studies, but there were no consistent results on gene expression patterns involved in PPARα activation in genome-wide due to different sample sizes or platforms. Here, we employed two published microarray datasets both PPARα dependent in mouse liver and applied meta-analysis on them to increase the power of the identification of differentially expressed genes and significantly enriched pathways. As a result, we have improved the concordance in identifying many biological mechanisms involved in PPARα activation. We suggest that our analysis not only leads to more identified genes by combining datasets from different resources together, but also provides some novel hepatic tissue-specific marker genes related to PPARα according to our re-analysis.

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“…Based on docking computations and MD simulations, Stjernschantz et al. explored SULT1E1 to find selective inhibitors and to explain their selectivity. Substrate selectivity profiles of SULT1 A3 and SULT1E1 were investigated by virtual screening experiments and it was shown that the structure‐based approach is useful for prediction of preferential substrate classes.…”

Section: D‐admet: Overall Concepts and Applications In Mtimentioning

confidence: 99%

Computational Biology and Chemistry in MTi: Emphasis on the Prediction of Some ADMET Properties

Villoutreix

2017

Molecular Informatics

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Our research and teaching group called MTi (Molécules Thérapeutiques in silico) has developed numerous applications available online, thanks to the RPBS platform (Ressource Parisienne en Bioinformatique Structurale), in the field of chemoinformatics, structural bioinformatics and drug design. Since its opening in 2009, over 200 articles/reviews have been reported and involve virtual screening studies, prediction of druggability, analysis of protein-protein interaction inhibitors, development of databases, data mining and knowledge discovery, as well as combined in silico-in vitro work to search for new hits and chemical probes acting on original targets in several therapeutic areas. An international training program has also been developed pertaining to the field of in silico drug design. In this review, we present some tools developed in our laboratory with a special emphasis on the prediction of some ADMET properties, compound collection preparation and 3D-ADMET computations.

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Comparison of murine and human estrogen sulfotransferase inhibition in vitro and in silico—Implications for differences in activity, subunit dimerization and substrate inhibition

Cited by 17 publications

References 61 publications

In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

A meta-analysis study of gene expression datasets in mouse liver under PPARα knockout

Computational Biology and Chemistry in MTi: Emphasis on the Prediction of Some ADMET Properties

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